Chemical Profiling, Antibacterial Efficacy, and Synergistic Actions of <i>Ptychotis verticillata</i> Duby Essential Oil in Combination with Conventional Antibiotics
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Bibliographic record
Abstract
Introduction: The primary aim of the current investigation is to assess the chemical composition and antibacterial efficacy of Ptychotis verticillata essential oil (PVEO) against three Gram-positive bacteria ( Staphylococcus aureus, Micrococcus luteus, and Bacillus subtilis) and three Gram-negative bacteria ( Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae). Furthermore, this research endeavors to investigate the potential synergistic effects of PVEO when combined with established antibiotics (Amoxicillin, Erythromycin, and Ampicillin), with a focus on discerning their interaction dynamics. Methods: The phytochemical profiling of PVEO was performed via the Gas Chromatography-Mass Spectrometry technique. The broth dilution test determined the interaction effect between PVEO and these three antibiotics. Results: Components γ-terpinene (38.96%), p-cymene (19.38%), thymol (18.07%), and carvacrol (6.99%) are the major bioactive moleculs composing this studied essential oil. PVEO demonstrated significant antibacterial activity with minimum inhibitory concentration (MIC) values ranging from 0.2 against E coli to 2 mg/mL against M luteus. The assessment of the synergistic activity between PVEO and antibiotics was accomplished through the utilization of the Fractional Inhibitory Concentration Index (FICI). The combination of PVEO and amoxicillin against M luteus demonstrated the best synergistic action with a FICI value of 0.43. Conclusion: Significant reductions, ranging from two to sevenfold, in the MIC values of PVEO, and antibiotics were observed. This noteworthy synergistic interaction between highly potent essential oils and synthetic antibiotics holds the potential to open avenues for novel combination therapies aimed at addressing infections caused by multiresistant microorganisms, even at notably reduced concentrations within the pharmaceutical sector.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it